IOP Conference Series: Materials Science and Engineering
PAPER • OPEN ACCESS
Renewable Energy Resources: Case Studies
To cite this article: Balaji Devarajan et al 2021 IOP Conf. Ser.: Mater. Sci. Eng. 1145 012026
View the article online for updates and enhancements.
This content was downloaded from IP address 185.36.195.50 on 26/05/2021 at 06:06
ICCMES 2021
IOP Publishing
IOP Conf. Series: Materials Science and Engineering
1145 (2021) 012026
doi:10.1088/1757-899X/1145/1/012026
Renewable Energy Resources: Case Studies
Balaji Devarajan1, V Bhuvaneswari1, A K Priya1, G Nambirajan1, J Joenas1,
P Nishanth1, L Rajeshkumar2, G Kathiresan3 and V Amarnath3
1
Department of Mechanical Engineering, KPR Institute of Engineering and
Technology, Coimbatore, Tamilnadu 641407, India
2
Head R&D Shanthi gears, Coimbatore, Tamilnadu 641406, India
Sri Ramakrishna Engineering College, Coimbatore, Tamilnadu 641022, India
1
balaji.ntu@gmail.com
3
Abstract. The energy need is the only demand which wouldn’t have seen negative trend since
the origin of this universe. Its requirement keeps demanding the usage of energy, during this
urge people around globe working with many energy production techniques. Amongst most of
them act as a resource including fossil fuel, coal and others are polluting vicinity to larger
extend. The other alternative is renewable energy resources (RERs) which quite natural gift to
the mankind owing to its vicinity aiding resource. The energy harvesting by utilising these
RERs also have limitation that, can’t provide huge in quantity due to many reasons
including seasonal, inadequate equipment, larger storage so on and so forth. The focus herein
is that, by considering its limitations to which extend it can be utilised. It is obvious that
production industries require enormous quantity of power, therein it may not be utilised as
such. So, the house as well as small industries whose power requirement is minimum thereby
this RERs can be effectively utilised. That is considered as a primary factor for consolidating of
this survey in the form of various test cases.
Keywords: RER Case study; RER for House
1. Introduction
1.1. Power generation - Decentralized
The planet is heading through energy production and saving. The impediment for energy with both
energy production and saving. In almost all of instances, this is being depleted by several ways. Is the
industry which is the one wasting the resources? The response may be slightly true, now most of the
industries by some management concepts they have minimized it. To conflicting, overall energy loss
requires in homes and residential buildings. In several apartments' effective utilization is 365 days a year
X 24 hours a day X 7 days a week. The scenario is considered herein, wherever feasible the clean power
can be substituted. Some of these scenarios are integrated for future conservation of electricity. This one
in turn should save world in broader perspective. Many of them seem to be including, the numerous
testimonials are examined linked to usage of electricity in village areas, impacts of decentralised energy
production, hybrid clean renewable energy grid to satisfy domestic needs, to brighten up village lives
including off-grid energy production and job prospects for local youth, and so on, which are tailored
with the help of different technology like LINDO, VIPOR, HOMER, HYPORA, and so on [1-5].
1.2. Prosumer-based energy management
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution
of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Published under licence by IOP Publishing Ltd
1
ICCMES 2021
IOP Publishing
IOP Conf. Series: Materials Science and Engineering
1145 (2021) 012026
doi:10.1088/1757-899X/1145/1/012026
The architecture for such a 2-stage optimization procedure for optimum scheduling of smart home
(RERs - renewable energy resources) and storage incorporation with prosumer-based energy regulation.
During the 1st-stage of optimization, the optimum sizing issue of in-house RERs and storage is discussed
with the aim function of RERs and storage of the life cycle cost is completely limiting. In the second
stage, the smart home regular energy activity issue is formulated using multiobject home energy
management (MOHEM) [6].
1.3. Home Energy Management Systems (HEMS)
The explanation for evaluating a society as a single energy consumption system is that a community at
large consolidated energy production or utilization is far more consistent compared to a household power
usage unit, even as arbitrary activity of independent power users has very little effect also on community
total power usage mode. Thus, app roaching an entire population as just a single unit would enhance the
predictability of overall power usage, as statistical information is much more accurate and consistent in
representing the population 's exact electricity needs. More significantly, coping towards a community's
request is further realistic in the resource conservation framework than coping with private homes'
demands. [7-9]. The following images reveals the proposed system. Figure 1 shows the Home power
management.
Figure 1. Home power management
Figure 2. HEMS outline and linking
Figure 3. HEMS regulator
With introduction of technological advancements in the area of sustainable energy and storage,
growing number of buildings are being fitted via renewable energy sources (RES) and energy storage
systems (ESS) to minimize home power consumption price. Figure 2 shows the HEMS outline and
linking. These households typically have HEMS to monitor and plan each electrical unit. Numerous
studies were performed on HEMS and efficiency algorithms for power expense and peak-to-average
ratio (PAR) mitigation. Figure 3 shows the HEMS regulator. Even so, many of papers offer an enough
study on the usage of main grid 's electricity and selling electricity [10-13]. Figure 4 shows the HEMS
architecture.
2
ICCMES 2021
IOP Publishing
IOP Conf. Series: Materials Science and Engineering
1145 (2021) 012026
doi:10.1088/1757-899X/1145/1/012026
Figure 4. HEMS architecture
1.4 Smart building
Thomas et al. presented a design for efficient process of an Energy Management System in a residential
home with the help of RERs, battery energy storage (BES) method, as well as Plugin Electric Vehicles
(PHEVs) under static and dynamic techniques. The issue was fixed with CPLEX optimization method
herein [14,15]. Figure 5 shows the Configuration of smart building EMS. Figure 6 shows the hardware
design.
Figure 5. Configuration of smart building EMS
Figure 6. hardware design
1.5 Demand Side Management (DSM)
Implementing proper energy conservation strategies and using RERs improve future grid systems'
energy stability and security. This research suggested a framework for residential energy management
comprising of microgrid structure and DSM - Demand Side Management method. To decrease peak
load, peak to average, and energy cost, loads of residences were moved dependent on price-based tariff
including such adjustable and moment-of-use tariffs. Renewable energy incorporation with DSM can
become a fruitful solution to reducing households' overall power costs by paying very little for buying
electricity and exporting the excess power to the grid. The latest investigation tends to reduce financial
expenses of residential electricity use among moving loads to periods with smaller electricity costs and
using renewable energy. A domestic load management framework was suggested dependent on costbased demand response and RER integration. Computation and scenario were performed in various
households to evaluate the efficiency of the suggested framework under versatile and billing practices
by utilizing BPSO algorithm through MATLAB. [16-18].
1.6 Vehicle to home
Electric vehicle (EV) has been one of the potential ways to fix emissions and environmental warming
[19,20]. EV also utilizes on-board energy storage device for power to run the motor. The super capacitors
and lithium-ion battery are typical refillable modules on the EVs [21]. Fuel cell electric vehicle (FCEV)
is often known as EVs. The FCEV is driven by hydrogen, along with its on-board motor utilizes fuel
cells. The Hybrid EV incorporates both rechargeable storage device as well as fuel cell energy [22].
Perhaps one EV's good features should be to perform as vehicle-to -grid (V2G) [23]. In this process, the
EV can indeed be disposed to transfer energy to its grid. The V2G process helps EVs can provide peak
load and diminishes the energy costs. The V2 G may indeed charge and discharge renewable energy
surplus to smooth intermittent renewables. Under uncertainty, certain vehicles may be the right backup
power source [24]. Vehicle-to-home (V2H) would be the same as V2G. In V2H, the EV is attached to
the house, and the property owner optimises its charging as well as discharge regime. V2H will provide
residential energy requirements when energy costs like peakhours. After emergency events, V2H can
act as battery backup resource [25]. V2H could be a potential solution for improving energy savings in
home energy management (HEMS) [26]. HEMS is a model that handles residential energy consumption
[27] by different techniques such as process optimization [28], energy device to store [29] and renewable
deployment [30], and demand-based response programs [31]. HEMS may be operated on gridconnected premises or removed from the electricity network, i.e., NZEB - Net Zero Energy Building.
HEMS frequently uses different renewables including wind, sun energy, hydro, power cell [19]. It also
3
ICCMES 2021
IOP Conf. Series: Materials Science and Engineering
IOP Publishing
1145 (2021) 012026
doi:10.1088/1757-899X/1145/1/012026
operates various Energy Storage Systems (ESSs) [32], capacitor, heat and ice storage systems [33].
Resilience and self-healing are the latest principles of building energy management [34]. In recent times,
the problems facing electrical grid systems are substantially different from that of a decade earlier. As a
consequence, electronic components developed novel concepts like resilience [35] and self-healing [36].
More so than ever, a robust network that can withstand hazardous incidents and disruptions like cyber
threats and adverse weather is important. At same time, unit’s operation is drastically different by
integrating emerging technology such as renewable energy, ESSs, and demand response programmes.
Resilience is described as "the ability to anticipate, plan and adjust to changing conditions and recover
quickly from disturbances [37]. Resilience in power grids and networks has been already established
and several app roaches are being suggested to enhance resilience such as grid reconfiguration [38],
power storage integration and decentralized source of energy use [39]. Self-healing is channel's way to
recognise and detach failures and reorganise the network to mitigate consumers. Multi-agent
optimization is among the popular solutions for self-healing network [40]. Many electrical grid
controller actions in electrical systems include stress and pressure accompanying unusual high-impact
(HR) events. Consequently, a detailed image of network status is vital. As a consequence, an adequate
framework is required to define the model's various states and draw informed choices from a resilienceoriented perspective [41]. Renewable technologies are among the successful solutions that meet device
resilience criteria. To model a robust microgrid, stochastic programming will model natural disasters
[30]. Microgrid facilities will boost network reliability to tackle daily outages [42]. Hasan Mehrjerdi et.
al., presents a novel version for HEMS like demand response programme, electric car, diesel generator
and wind turbine. The aim is to reduce regular operating value of the construction. The EV functions on
V2H and this can provide the peak load of both the house. The power generation software designs a
deflectable load and gets involved throughout the energy efficiency system. The diesel generating
system and V2H act as backup supp ly for both the house. The statistical modelling increases the stability
of the structure during deficiencies and incidents like power production or wind system outage. [43].
1.7 Hybrid Microgrids
The hybrid AC / DC smart grid is regarded as rising loads in energy systems. I n this research, it has
been shown that the hybrid AC / DC smart grid is constructed in the customer's home with certain RES
(e.g., solar energy, wind energy) to remain competitive. Strom production and utilization undergo a
major transition. One tendency is to incorporate smart grids with increasing capacity of renewable
energies into the distribution channel. [44]. At device level, energy consumption is designed to maintain
balance of power within different circumstances of production and demand. Coordinated management
of integrated smart grids with RERs also demonstrates the efficacy of this methodology to provide
continuous services to customers through different controller designs. Proposed control MPP T
technique is often implemented for both solar cells and wind turbines to obtain full power from either
the hybrid energy storage system. [45].
1.8 Smart home energy management system (SHEMS)
Current SHEMS is inherently vulnerable to cyber-attack, so it needs robust cyber-attack strategies.
Currently SHEMS situation can lead to increased charging and discharge cycles degrading battery
capacity. Consequently, request planning formulations must also compensate for the impact of battery
discharge costs alongside user convenience. The present work aims to devise a detailed scheduling issue
in terms of minimising electricity prices, given battery depletion costs. This study also suggests a cyberattack resilient scheduling scheme. This article discusses the impact of request scheduling on battery life
and energy costs. Furthermore, False Data Injection Attack (FDIA) was designed by utilizing machine
learning [46].
1.9 Stochastic renewable energy resources
Renewable energy and transportation modernisation are becoming critical components of current and
future delivery planning. Even after the momentum of environmental and economic benefits, the
complexity presents challenges. It introduces an interconnected growth planning system refers to a
multi-objective variational mixed-integer programme. The goal is to reduce the current value of
4
ICCMES 2021
IOP Conf. Series: Materials Science and Engineering
IOP Publishing
1145 (2021) 012026
doi:10.1088/1757-899X/1145/1/012026
expenditures including feeder distribution, substation changes and building, while optimising the usage
of suggested charging points. [47].
1.10 AI in renewable energy home system
This study constructs a feasibility analysis to describe the energy efficiency house issue and house
automation system deployment. Consequently, the options were extensively examined to decide the
critical things required for this kind of work. Energy efficiency may play a prominent part in lowering
electrical costs, and Smart Energy House's extension enabled it much more special and difficult to
introduce. In addition, the suggestions were analysed and clearly argued to reduce the temperature effect
and minimise the effect in the source of power by regulating the linked load further into device. A basic
working capital modelling was completed, and that was clear that the system becomes viable to
incorporate, along with all material costs and placing the board operating costs. Could not only this work
play a significant role in sustainable house with automation framework. The Levelized power cost
became determined according to output by the system's overall initial investment [48-51].
1.11 Integrated GWSS – (grey wolf with shark smell) algorithms
This research explores the impact of microgrid (MG) sensitive load integration. 3 scenarios were
evaluated for detailed description of the issues. Outcomes indicated that better the degree of smart load
infiltration will affect system reliability, voltage variance, and load waveform. This analysis also
introduces a mixture of GWSS algorithms to optimise fitness function within multiple boundaries
restrictions, and the suggested technique does have a high consistency to global minimum. An updated
33-bus MG involves wind, PV and energy storage systems. Using hybrid GWSS integration, the impacts
of green energy and energy storage integration were also studied. [52-56].
1.12 Renewable energy integration
Renewables also rose to prominence owing to conservation and less effects on the environment. With
rising power consumption and replenishing carbon fuels, emphasis was placed towards creating eco friendly energy solutions as well as widening the utilization of renewable energy. Energy integration
solutions, which would satisfy energy requirements in varied forms, are deemed a viable solution to
rising the use of RERs and improving production performance. In recent times, integrated power and
heat solutions are being linked to energy performance and fewer impact on the environment. [57,58].
1.13 Geopolitical Perspective
This analysis examined the importance of clean energy in forming resource protection amid world
economic, societal, and technical uncertainty. At first, the evolving definition of resource preservation
even during 20th and initial 21st centuries being explored. Parameters, features and indicators of energy
security being tested, except 4A definition of energy security comprising availability; financial
affordability; socio-political ease of access; and ecological app ropriateness. [59].
1.14 Modelling and simulation
This study demonstrates trying to model and simulating a renewable power smart-grid structure. It
developed with the aid of a wind turbine (WT) installed on a dual-fed generator (DFIG), a PV, a fuel
cell (FC) generating system, a hydrogen reservoir, a long-term storage of water electrolyser as well as a
short-term storage system. These authors propose a strategy for global regulation and total energy
conservation of devices. [60].
2. Conclusion
These kinds of case study analysis clearly provide vision towards the selection of optimal method of
renewable energy resources for home. The test cases are taken from the perspective by considering
parameters like smart house management, simulation-based systems, hybrid RERs and vehicle to home
connectivity.
5
ICCMES 2021
IOP Conf. Series: Materials Science and Engineering
IOP Publishing
1145 (2021) 012026
doi:10.1088/1757-899X/1145/1/012026
References
[1] Deshwar, C.S., Sharma, R., Gupta, A.K. and Singh, M.S., 2020. Assessment and Scope of
Decentralised Power Generation Using Renewable Energy Resources. Available at SSRN
3554854.
[2] Bhamidi, L. and Sivasubramani, S., 2020. Optimal sizing of smart home renewable energy
resources and battery under prosumer-based energy management. IEEE Systems Journal.
[3] Mahela, O.P , Khosravy, M., Gupta, N., Khan, B., Alhelou, H.H., Mahla, R., Patel, N. and
Siano, P , 2020. Comprehensive overview of multi-agent systems for controlling smart grids.
CSEE Journal of Power and Energy Systems.
[4] Dinh, H.T., Yun, J., Kim, D.M., Lee, K.H. and Kim, D., 2020. A home energy management
system with renewable energy and energy storage utilizing main grid and electricity selling.
IEEE Access, 8, pp 49436-49450.
[5] Thomas, D., Deblecker, O. and Ioakimidis, C.S., 2018. Optimal operation of an energy
management system for a grid-connected smart building considering photovoltaics’
uncertainty and stochastic electric vehicles’ driving schedule. App lied Energy, 210, pp 11881206.
[6] M Ramesh, C Deepa, M Tamil Selvan, L Rajeshkumar, D Balaji, V Bhuvaneswari 2020
Mechanical and water absorption properties of Calotropis gigantea plant fibers reinforced
polymer
composites,
Materials
Today
Proceedings
https://doi.org/10.1016/j.matpr.2020.11.480.
[7] Rajesh kumar L, Saravanakumar A, Bhuvaneswari V, JithinKarunan M P, Karthick raja N,
Karthi P 2020 Tribological Behaviour of AA2219/MoS2 Metal Matrix Composites under
Lubrication AIP Conference Proceedings2207 020005.
[8] V Bhuvaneswari, M Priyadharshini, C Deepa, D Balaji, L Rajeshkumar, M Ramesh 2021 Deep
learning for material synthesis and manufacturing systems A review, Materials Today:
Proceedings, https://doi.org/10.1016/j.matpr.2020.11.351.
[9] S, D., & H, A. (2019). AODV Route Discovery and Route Maintenance in MANETs. 2019
5th International Conference on Advanced Computing & Communication Systems (ICACCS).
doi:10.1109/icaccs.2019.8728456.
[10] H. Anandakumar and K. Umamaheswari, An Efficient Optimized Handover in Cognitive
Radio Networks using Cooperative Spectrum Sensing, Intelligent Automation & Soft
Computing, pp. 1–8, Sep. 2017. doi:10.1080/10798587.2017.1364931.
[11] PrakashDuraisamy, XiaohuiYuan, ElSaba,A. and Sumithra Palanisamy, Contrast
enhancement and assessment of OCT images, Proceedings of International Conference on
Informatics, Electronics & Vision (ICIEV), 2012 Date: 18-19 May 2012 pp.91-95(Location
:Dhaka, Print ISBN: 978-1-4673-1153-3,INSPEC Accession Number: 13058449,Digital
Object Identifier :10.1109/ICIEV.2012.6317381).
[12] Sumithra M. G., Thanushkodi, K. and Helan Jenifer Archana ,A. A New Speaker Recognition
System with Combined Feature Extraction Techniques , Journal of Computer Science, Vol. 7,
Issue 4, pp.459- 465, 2011. (With impact factor SNIP of 0.162 and SJR of0.034)
[13] Balasaraswathi, M., Srinivasan, K., Udayakumar, L., Sivasakthiselvan, S. and Sumithra, M.G.,
2020. Big data analytic of contexts and cascading tourism for smart city. Materials Today:
Proceedings
[14] Sivakumar, P., Boopathi, C.S., Sumithra, M.G., Singh, M., Malhotra, J. and Grover, A., 2020.
Ultra-high capacity long-haul PDM-16-QAM-based WDM-FSO transmission system using
coherent detection and digital signal processing. Optical and Quantum Electronics, 52(11),
pp.1-18.
[15] Noel, L., Carrone, A.P , Jensen, A.F., de Rubens, G.Z., Kester, J. and Sovacool, B.K., 2019.
Willingness to pay for electric vehicles and vehicle-to-grid app lications: A Nordic choice
experiment. Energy Economics, 78, pp 525-534.
6
ICCMES 2021
IOP Conf. Series: Materials Science and Engineering
IOP Publishing
1145 (2021) 012026
doi:10.1088/1757-899X/1145/1/012026
[16] Mehrjerdi, H. and Rakhshani, E., 2019. Vehicle-to-grid technology for cost reduction and
uncertainty management integrated with solar power. Journal of Cleaner Production, 229, pp
463-469.
[17] Liu, C., Chau, K.T., Wu, D. and Gao, S., 2013. Opp ortunities and challenges of vehicle-tohome, vehicle-to-vehicle, and vehicle-to-grid technologies. Proceedings of the IEEE, 101(11),
pp 2409-2427.
[18] Ramesh M., RajeshKumar L., Bhuvaneshwari V. 2021 Bamboo Fiber Reinforced Composites
Bamboo Fiber Composites. Composites Science and Technology
[19] M. Ramesh, L. Rajeshkumar 2018, Wood flour filled thermoset composites Thermoset
composites: preparation, properties and app lications (vol 4)ed Anish Khan, Showkat Ahmad
Bhawani et al (USA: Materials Research Foundations) pp 33–65
[20] M. Ramesh, L.R. Kumar, A. Khan, A.M. Asiri 2020 Self-healing polymer composites and its
chemistry, Self-Healing Compos. Mater.edAnish Khan, Mohammad Jawaid et al
(Amsterdam:Elsevier) pp 415-427
[21] Mehrjerdi, H. and Rakhshani, E., 2019. Vehicle-to-grid technology for cost reduction and
uncertainty management integrated with solar power. Journal of Cleaner Production, 229, pp
463-469.
[22] Šadauskienė, J., Šeduikytė, L., Paukštys, V., Banionis, K. and Gailius, A., 2016. The role of
air tightness in assessment of building energy performance: Case study of Lithuania. Energy
for Sustainable Development, 32, pp 31-39.
[23] Hemmati, R. and Saboori, H., 2017. Stochastic optimal battery storage sizing and scheduling
in home energy management systems equipp ed with solar photovoltaic panels. Energy and
Buildings, 152, pp 290-300.
[24] Hemmati, R. and Azizi, N., 2017. Nonlinear modeling and simulation of battery energy storage
systems incorporating multiband stabilizers tuned by Meta-heuristic algorithm. Simulation
Modelling Practice and Theory, 77, pp 212-227.
[25] Saravana Kumar A, P MaivizhiSelvi, L Rajeshkumar 2017 Delamination in Drilling of
Sisal/Banana Reinforced Composites Produced by Hand Lay-Up Process App lied Mechanics
and Materials, 867, pp 29-33.
[26] Rajeshkumar, L R K 2018 Dry sliding wear behavior of AA2219 reinforced with magnesium
oxide and graphite hybrid metal matrix composites Int J Eng Res Technol,6, pp 3-8.
[27] Hemmati, R., 2017. Technical and economic analysis of home energy management system
incorporating small-scale wind turbine and battery energy storage system. Journal of Cleaner
Production, 159, pp 106-118.
[28] Voulis, N., van Etten, M.J., Chapp in, É.J., Warnier, M. and Brazier, F.M., 2019. Rethinking
European energy taxation to incentivise consumer demand response participation. Energy
policy, 124, pp 156-168.
[29] N Anbuchezhian, M Priyadharshini, BalajiDevarajan, A K Priya, L Rajeshkumar 2020,
Machine Learning Frameworks for Additive Manufacturing – A Review, Solid State
Technology, 63 (6) 12310-12319.
[30] V Bhuvaneswari, L Rajeshkumar, D Balaji, R Saravanakumar 2020, Study of Mechanical and
Tribological Properties of Bio-Ceramics Reinforced Aluminium Alloy Composites, Solid
State Technology, 63 (5) 4552-4560.
[31] Yang, X., Zhang, S. and Xu, W., 2019. Impact of zero energy buildings on mediumto-long
term building energy consumption in China. Energy Policy, 129, pp 574-586.
[32] Mehrjerdi, H., 2019. Simultaneous load leveling and voltage profile improvement in
distribution networks by optimal battery storage planning. Energy, 181, pp 916-926.
[33] Beaudin, M. and Zareipour, H., 2015. Home energy management systems: A review of
modelling and complexity. Renewable and sustainable energy reviews, 45, pp 318335.
7
ICCMES 2021
IOP Conf. Series: Materials Science and Engineering
IOP Publishing
1145 (2021) 012026
doi:10.1088/1757-899X/1145/1/012026
[34] L Rajeshkumar, R Suriyanarayanan, K Shree Hari, S VenkateshBabu, V Bhuvaneswari, M P
JithinKarunan 2020, Influence of Boron Carbide Addition on Particle Size of Copp er Zinc
Alloys Synthesized by Powder Metallurgy, IOP Conf. Ser.: Mater. Sci. Eng. 954 012008.
[35] L Rajeshkumar, V Bhuvaneswari, B Pradeepraj, M Praveen pauldurai, C Palanivel, S Sandeep
kumar 2020, Design and Optimization of Static Characteristics for a Steering System in an
ATV, IOP Conf. Ser.: Mater. Sci. Eng., 954 012009.
[36] Rosales-Asensio, E., de Simón-Martín, M., Borge-Diez, D., Blanes-Peiró, J.J. and ColmenarSantos, A., 2019. Microgrids with energy storage systems as a means to increase power
resilience: An app lication to office buildings. Energy, 172, pp 10051015.
[37] Manshadi, S.D. and Khodayar, M.E., 2015. Resilient operation of multiple energy carrier
microgrids. IEEE Transactions on Smart Grid, 6(5), pp 2283-2292.
[38] Cavalcante, P L., López, J.C., Franco, J.F., Rider, M.J., Garcia, A.V., Malveira, M.R., Martins,
L.L. and Direito, L.C.M., 2015. Centralized self-healing scheme for electrical distribution
systems. IEEE transactions on smart grid, 7(1), pp 145-155.
[39] Rajesh Kumar, L Saravanakumar, A Bhuvaneswari V, Gokul G, Dinesh Kumar D,
JithinKarunan, M P 2020 Optimization of wear behaviour for AA2219-MoS2 metal matrix
composites in dry and lubricated condition Mater. Today: Proc. 27(3), 2645– 2649
[40] Rajesh Kumar L; Amirthagadeswaran, K S 2020 Corrosion and wear behaviour of nano Al2O3
reinforced copp er metal matrix composites synthesized by high energy ball milling Part Sci.
Technol.38(2), 228–235
[41] Sutton-Grier, A.E., Wowk, K. and Bamford, H., 2015. Future of our coasts: The potential for
natural and hybrid infrastructure to enhance the resilience of our coastal communities,
economies and ecosystems. Environmental Science & Policy, 51, pp 137-148.
[42] Rajesh kumar L, S Sivalingam, K Prashanth, K Rajkumar 2020, Design, Analysis and
Optimization of Steering Knuckle for All Terrain Vehicles, AIP Conference Proceedings,
2207, pp 020003.
[43] Saravanakumar A, Rajeshkumar L, Balaji D et al 2020 Prediction of Wear Characteristics of
AA2219-Gr Matrix Composites Using GRNN and Taguchi-Based App roach. Arab J SciEng,
45 9549–9557
[44] Ding, T., Lin, Y., Bie, Z. and Chen, C., 2017. A resilient microgrid formation strategy for load
restoration considering master-slave distributed generators and topology reconfiguration. App
lied energy, 199, pp 205-216.
[45] Arghandeh, R., Von Meier, A., Mehrmanesh, L. and Mili, L., 2016. On the definition of cyberphysical resilience in power systems. Renewable and Sustainable Energy Reviews, 58, pp
1060-1069.
[46] Beheshtaein, S., Savaghebi, M., Vasquez, J.C. and Guerrero, J.M., 2015, November.
Protection of AC and DC microgrids: challenges, solutions and future trends. In IECON 201541st Annual Conference of the IEEE Industrial Electronics Society (pp 005253-005260).
IEEE.
[47] A Saravanakumar, S Sivalingam, L Rajesh kumar 2016 Dry sliding wear behaviour of
AA2219/Gr metal matrix composites, Mater. Today: Proc. 5 8321–8327.
[48] L Rajeshkumar, K S Amirthagadeswaran 2019 Variations in the properties of copp eralumina
nanocomposites synthesized by mechanical alloying, Mater.Technol. 59 (1) 57–63.
[49] Gholami, A., Shekari, T., Amirioun, M.H., Aminifar, F., Amini, M.H. and Sargolzaei, A.,
2018. Toward a consensus on the definition and taxonomy of power system resilience. IEEE
Access, 6, pp 32035-32053.
[50] Gholami, A., Shekari, T., Aminifar, F. and Shahidehpour, M., 2016. Microgrid scheduling
with uncertainty: The quest for resilience. IEEE Transactions on Smart Grid, 7(6), pp 28492858.
8
ICCMES 2021
IOP Conf. Series: Materials Science and Engineering
IOP Publishing
1145 (2021) 012026
doi:10.1088/1757-899X/1145/1/012026
[51] Gholami, A., Aminifar, F. and Shahidehpour, M., 2016. Front lines against the darkness:
Enhancing the resilience of the electricity grid through microgrid facilities. IEEE
Electrification Magazine, 4(1), pp 18-24.
[52] L Rajesh kumar, A Saravanakumar, R Saravanakumar, S Sivalingam, V Bhuvaneswari 2018
Prediction capabilities of mathematical models for wear behaviour of AA2219/MgO/Gr
hybrid metal matrix composites International Journal of Mechanical and Production
Engineering Research and Development8 393-399.
[53] Mehrjerdi, H. and Hemmati, R., 2020. Coordination of vehicle-to-home and renewable
capacity resources for energy management in resilience and self-healing building. Renewable
Energy, 146, pp 568-579.
[54] Sethi, B.K., Mukherjee, D., Singh, D., Misra, R.K. and Mohanty, S.R., 2020. International
Transactions on Electrical Energy Systems, p e12411.
[55] Fan, V.H., Dong, Z. and Meng, K., 2020. Integrated distribution expansion planning
considering stochastic renewable energy resources and electric vehicles. App lied Energy, 278,
p 115720.
[56] Islam, S., Rojek, L., Hartmann, M. and Rafajlovski, G., 2020, September. Artifical Intelligence
in Renewable Energy Systems Based on Smart Energy House. In IEEE Conference, Rec
(49733, pp 17-18).
[57] Seifi, A., Moradi, M.H., Abedini, M. and Jahangiri, A., 2020. Journal of Energy Storage, 27,
p 101126.
[58] Bagherian, M.A. and Mehranzamir, K., 2020. A comprehensive review on renewable energy
integration for combined heat and power production. Energy Conversion and Management,
224, p 113454.
[59] Paravantis, J.A. and Kontoulis, N., 2020. Energy Security and Renewable Energy: A
Geopolitical Perspective. Resources, Challenges and App lications, p 19.
[60] Tamalouzt, S., Benyahia, N., Tounzi, A. and Bousbaine, A., 2020. Performances Analysis of
a Micro-Grid Connected Multi-Renewable Energy Sources System Ass
9